Block-Based GPU Image Data Decompression
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Solution Overview
Problem
Current data compression methods for image data in graphics processing units (GPUs) face challenges in achieving efficient memory bandwidth utilization and power management, particularly in mobile devices, due to varying compression ratios and the need for random access to compressed data, which complicates memory allocation and access latency.
Innovation Solution
A method of data compression and decompression using a block-based encoding scheme that processes pixel data in raster scan order, allowing for efficient compression and decompression operations by reducing the amount of buffering required and simplifying hardware implementation, with guaranteed compression ratios or fixed block sizes, and embedding control data within the compressed data for efficient unpacking and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data compression is applied to image data in GPUs, then memory bandwidth utilization is improved and power consumption is reduced, but compression ratio variability and random access requirements complicate memory allocation and increase access latency
Solution Approach 1:
The patent divides the compressed data into fixed-size blocks with predetermined dimensions. Each block is independently compressed and stored, allowing for systematic memory allocation and access patterns. This segmentation enables the system to manage compressed data in organized units rather than dealing with variable-length compressed streams, thereby reducing memory allocation complexity while maintaining compression benefits.
2Manufacturing precision
If higher quality rendering algorithms are used on faster GPUs, then rendering quality is improved, but memory bandwidth consumption increases
Solution Approach 1:
The patent employs adjustable compression ratios and block sizes that can be optimized for different rendering quality requirements. By changing the compression parameters (ratio, block dimensions), the system can balance between rendering quality and memory bandwidth consumption, allowing high-quality rendering with reduced memory traffic through appropriate parameter selection.
3Productivity
If compression ratios are increased to reduce data transfer, then memory bandwidth is reduced, but decompression complexity and processing time increase
Solution Approach 1:
By segmenting the data into fixed-size blocks, the decompression process can operate on small, manageable units in parallel. This block-based approach reduces decompression complexity compared to processing entire frames or large data streams, as each block can be independently and simultaneously decompressed, thereby maintaining high data transfer efficiency without excessive decompression complexity.
4Adaptability or versatility
If random access to compressed data is enabled, then processing flexibility is improved, but access latency and memory overhead increase
Solution Approach 1:
The fixed-block structure enables efficient random access by allowing the system to directly jump to specific block indices without processing preceding data. Each block is self-contained with its own compression metadata, enabling independent access and processing. This segmentation maintains processing flexibility for random access operations while minimizing access latency compared to sequential or variable-length compression schemes.
Data Source
AI summary
Compressed image data is received in substantially in raster scan order, and for each group of pixels in a row of the compressed image data, a block-based decoding scheme for the group of pixels is identified and the compressed data corresponding to the group of pixels is decoded at decoding hardware using the identified scheme.